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| IdeaGraph plus: A Topic-Based Algorithm for Perceiving Unnoticed Events | |
| Zhang, Chen; Wang, Hao; Xu, Fanjiang; Hu, Xiaohui | |
| 2013 | |
| Conference Name | IEEE 13th International Conference on Data Mining (ICDM) |
| Pages | 735-741 |
| Conference Date | DEC 07-10, 2013 |
| Conference Place | Dallas, TX |
| Indexed Type | CPCI |
| Publish Place | IEEE |
| ISSN | 1550-4786 |
| ISBN | 978-0-7695-5109-8 |
| Department | [Zhang, Chen; Wang, Hao; Xu, Fanjiang; Hu, Xiaohui] Chinese Acad Sci, State Key Lab Comp Sci, Inst Software, Beijing 100190, Peoples R China. |
| English Abstract | In the last few years, chance discovery as an extension of data mining has been proposed to capture rare but significant chances from a single document data for human decision making. Key Graph is a useful miner algorithm as well as a tool to discover chance candidates. On base of that, Idea Graph extended the concept of a chance to uncover more valuable chances. However, Key Graph and Idea Graph both fail to consider semantic relations among terms. In this paper, we propose an improved algorithm called Idea Graph plus which makes use of semantic information to enhance the performance of scenario construction using LDA topic model. Additionally, the term overlaps between sub-scenarios provide a thinking space for human to perceive unnoticed chances. An experiment demonstrates the superiority of Idea Graph plus by comparing with IdeaGraph.; In the last few years, chance discovery as an extension of data mining has been proposed to capture rare but significant chances from a single document data for human decision making. Key Graph is a useful miner algorithm as well as a tool to discover chance candidates. On base of that, Idea Graph extended the concept of a chance to uncover more valuable chances. However, Key Graph and Idea Graph both fail to consider semantic relations among terms. In this paper, we propose an improved algorithm called Idea Graph plus which makes use of semantic information to enhance the performance of scenario construction using LDA topic model. Additionally, the term overlaps between sub-scenarios provide a thinking space for human to perceive unnoticed chances. An experiment demonstrates the superiority of Idea Graph plus by comparing with IdeaGraph. |
| Keyword | Chance Discovery Knowledge Discovery Topic Model Idea Graph Plus Latent Information |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16504 |
| Collection | 中国科学院软件研究所 |
| Recommended Citation GB/T 7714 | Zhang, Chen,Wang, Hao,Xu, Fanjiang,et al. IdeaGraph plus: A Topic-Based Algorithm for Perceiving Unnoticed Events[C]. IEEE,2013:735-741. |
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